Skill UI

This router guides the testing of systemic flaws in business processes and state machines. It is essential when vulnerabilities stem from workflow abuse, race conditions, pricing flaws, or multi-step state attacks, rather than simple input sanitation issues. Use it to test complex business flows like payments, inventory management, and approval sequences.

This playbook is designed for advanced vulnerability assessment, covering flaws invisible to automated scanners. It guides testing for race conditions, complex workflow bypasses, state machine manipulation, financial parameter tampering (e.g., price/quantity manipulation), and coupon/credit abuse in multi-step applications. Essential for deep manual security testing.

A comprehensive methodology for identifying critical vulnerabilities in transactional applications (e-commerce, fintech, SaaS). This goes beyond standard OWASP Top 10 checks, focusing on flaws like workflow bypass, state machine violations, price manipulation, and role boundary abuse. It guides testers to map application flows, identify hidden endpoints, and quantify the potential financial impact of logic failures.

This skill guides the systematic triage of project issues and external pull requests (PRs). It moves development items through a defined state machine using roles (e.g., bug, enhancement) and states (e.g., ready-for-agent, wontfix). The process involves context gathering, cross-codebase verification, deep questioning (grilling), and generating comprehensive, agent-ready briefs or marking them for human implementation.

This tool manages isolated profiles for Codex CLI and Codex Desktop, ensuring that different accounts, projects, and local states do not interfere with each other. It provides dedicated CODEX_HOME directories, allowing users to securely switch contexts (e.g., work, personal, client) on a single machine without manual environment variable management.

Design comprehensive customer journeys using state machine logic. This tool maps out every touchpoint, defines transitions based on customer behavior, incorporates branching logic for personalization, and simulates expected outcomes across multiple communication channels (email, SMS, social, etc.) before implementation. Ideal for optimizing acquisition, retention, and advocacy strategies.

A comprehensive guide for auditing and improving the scientific rigor of hardware architectural evaluations (e.g., HPCA style). It emphasizes establishing a clear 'fidelity contract' defining tools, scope, and configurations. It requires that all performance claims are backed by credible instruments, mandates capturing full machine state provenance, ensuring honest baseline tuning, and reporting results via per-workload distributions rather than single means.

This guide outlines the rigorous methodology for ensuring that computational research results, especially those involving hardware architecture and simulation, are fully reproducible. It emphasizes pinning the entire result chain—from simulator commits and configuration files to workloads, sampling recipes, and statistics scripts—and mandates creating a manifest for every figure. Key practices include recording machine state for silicon runs and containerizing the environment to prevent data drift.

A comprehensive guide for academic authors detailing how to establish and maintain rigorous reproducibility in Machine Learning research papers (e.g., ICLR). It instructs on mapping every central claim to verifiable evidence, including recording seeds, variance, specific compute budgets (training vs. inference), data preprocessing scripts, and ethical considerations. The statement must function as a public contract for the scientific community.

This guide provides rigorous methodology for authors submitting security and machine learning research to top conferences. It emphasizes the need to 'freeze the world' of measurement by pinning target versions, microarchitectural states, and network conditions. It details how to report probabilistic evidence (like attack success rates) and manage ethical data release, ensuring findings remain valid and verifiable over time.

This guide provides a structured protocol for authors writing methods papers in statistics or econometrics. It teaches how to define the precise 'delta' (incremental contribution) of a new method by explicitly referencing 3-6 existing state-of-the-art techniques. It emphasizes positioning the work across both traditional econometric theory and modern machine learning/data science literature, ensuring the contribution is clearly stated against named prior methods, not as a general survey.

A comprehensive guide for refining machine learning research papers tailored for NeurIPS submission. It teaches how to structure introductions, calibrate claims with evidence, explicitly state limitations, and address mandatory academic checklist items (e.g., reproducibility, broader impact). Use this skill to transform technical writing into a reviewer-proof, high-impact academic manuscript.